7 papers
BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
Liulu He, Shenli Zheng, Karwei Sun +6
Rotations have become essential to state-of-the-art quantization pipelines for large language models (LLMs) by effectively smoothing outliers in weights and activations. However, f…
DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits
Chengjie Liu, Jiajia Li, Yabing Feng +5
Analog circuit design consists of the pre-layout and layout phases. Among them, the pre-layout phase directly decides the final circuit performance, but heavily depends on experien…
AnalogTester: A Large Language Model-Based Framework for Automatic Testbench Generation in Analog Circuit Design
Weiyu Chen, Chengjie Liu, Wenhao Huang +5
Recent advancements have demonstrated the significant potential of large language models (LLMs) in analog circuit design. Nevertheless, testbench construction for analog circuits r…
A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction
Chengjie Liu, Weiyu Chen, Huiyao Xu +3
In the design process of the analog circuit pre-layout phase, device sizing is an important step in determining whether an analog circuit can meet the required performance metrics.…
FBQuant: FeedBack Quantization for Large Language Models
Yijiang Liu, Hengyu Fang, Liulu He +4
Deploying Large Language Models (LLMs) on edge devices is increasingly important, as it eliminates reliance on network connections, reduces expensive API calls, and enhances user p…
AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting
Chengjie Liu, Weiyu Chen, Anlan Peng +3
Multi-stage amplifiers are widely applied in analog circuits. However, their large number of components, complex transfer functions, and intricate pole-zero distributions necessita…